Overview

Dataset statistics

Number of variables17
Number of observations2111
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)0.4%
Total size in memory1.3 MiB
Average record size in memory641.9 B

Variable types

Categorical5
Numeric8
Boolean4

Alerts

Dataset has 9 (0.4%) duplicate rowsDuplicates
Gender is highly overall correlated with Height and 1 other fieldsHigh correlation
Height is highly overall correlated with GenderHigh correlation
NObeyesdad is highly overall correlated with Gender and 2 other fieldsHigh correlation
Weight is highly overall correlated with NObeyesdad and 1 other fieldsHigh correlation
family_history_with_overweight is highly overall correlated with NObeyesdad and 1 other fieldsHigh correlation
CAEC is highly imbalanced (58.1%)Imbalance
SMOKE is highly imbalanced (85.4%)Imbalance
SCC is highly imbalanced (73.3%)Imbalance
MTRANS is highly imbalanced (57.1%)Imbalance
FAF has 411 (19.5%) zerosZeros
TUE has 557 (26.4%) zerosZeros

Reproduction

Analysis started2025-12-15 09:10:31.135130
Analysis finished2025-12-15 09:10:46.526177
Duration15.39 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Gender
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size127.9 KiB
Male
1068 
Female
1043 

Length

Max length6
Median length4
Mean length4.9881573
Min length4

Characters and Unicode

Total characters10530
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male1068
50.6%
Female1043
49.4%

Length

2025-12-15T14:10:46.664944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T14:10:46.817934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male1068
50.6%
female1043
49.4%

Most occurring characters

ValueCountFrequency (%)
e3154
30.0%
a2111
20.0%
l2111
20.0%
M1068
 
10.1%
F1043
 
9.9%
m1043
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e3154
30.0%
a2111
20.0%
l2111
20.0%
M1068
 
10.1%
F1043
 
9.9%
m1043
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e3154
30.0%
a2111
20.0%
l2111
20.0%
M1068
 
10.1%
F1043
 
9.9%
m1043
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e3154
30.0%
a2111
20.0%
l2111
20.0%
M1068
 
10.1%
F1043
 
9.9%
m1043
 
9.9%

Age
Real number (ℝ)

Distinct1402
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.3126
Minimum14
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-12-15T14:10:47.047706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17.891428
Q119.947192
median22.77789
Q326
95-th percentile38.09807
Maximum61
Range47
Interquartile range (IQR)6.052808

Descriptive statistics

Standard deviation6.3459683
Coefficient of variation (CV)0.26101562
Kurtosis2.826389
Mean24.3126
Median Absolute Deviation (MAD)3.22211
Skewness1.5291004
Sum51323.898
Variance40.271313
MonotonicityNot monotonic
2025-12-15T14:10:47.245232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18128
 
6.1%
26101
 
4.8%
2196
 
4.5%
2389
 
4.2%
1959
 
2.8%
2048
 
2.3%
2239
 
1.8%
1730
 
1.4%
2418
 
0.9%
2516
 
0.8%
Other values (1392)1487
70.4%
ValueCountFrequency (%)
141
 
< 0.1%
151
 
< 0.1%
169
0.4%
16.0932341
 
< 0.1%
16.1292791
 
< 0.1%
16.1729921
 
< 0.1%
16.1981531
 
< 0.1%
16.2405761
 
< 0.1%
16.2704341
 
< 0.1%
16.306872
 
0.1%
ValueCountFrequency (%)
611
< 0.1%
561
< 0.1%
55.246251
< 0.1%
55.1378811
< 0.1%
55.0224941
< 0.1%
552
0.1%
521
< 0.1%
511
< 0.1%
50.8325591
< 0.1%
47.70611
< 0.1%

Height
Real number (ℝ)

High correlation 

Distinct1574
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7016774
Minimum1.45
Maximum1.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-12-15T14:10:47.475686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile1.5482905
Q11.63
median1.700499
Q31.768464
95-th percentile1.85
Maximum1.98
Range0.53
Interquartile range (IQR)0.138464

Descriptive statistics

Standard deviation0.09330482
Coefficient of variation (CV)0.054831088
Kurtosis-0.56294889
Mean1.7016774
Median Absolute Deviation (MAD)0.069769
Skewness-0.012854646
Sum3592.2409
Variance0.0087057894
MonotonicityNot monotonic
2025-12-15T14:10:47.764183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.760
 
2.8%
1.6550
 
2.4%
1.643
 
2.0%
1.7539
 
1.8%
1.6236
 
1.7%
1.828
 
1.3%
1.7219
 
0.9%
1.6317
 
0.8%
1.6716
 
0.8%
1.7815
 
0.7%
Other values (1564)1788
84.7%
ValueCountFrequency (%)
1.451
 
< 0.1%
1.4563461
 
< 0.1%
1.481
 
< 0.1%
1.4816821
 
< 0.1%
1.4832841
 
< 0.1%
1.4864841
 
< 0.1%
1.4894091
 
< 0.1%
1.4914411
 
< 0.1%
1.4985611
 
< 0.1%
1.513
0.6%
ValueCountFrequency (%)
1.981
< 0.1%
1.9756631
< 0.1%
1.9474061
< 0.1%
1.9427251
< 0.1%
1.9312631
< 0.1%
1.9304161
< 0.1%
1.932
0.1%
1.921
< 0.1%
1.9195431
< 0.1%
1.9188591
< 0.1%

Weight
Real number (ℝ)

High correlation 

Distinct1525
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.586058
Minimum39
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-12-15T14:10:48.188105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile48.5
Q165.473343
median83
Q3107.43068
95-th percentile131.91615
Maximum173
Range134
Interquartile range (IQR)41.957339

Descriptive statistics

Standard deviation26.191172
Coefficient of variation (CV)0.30248717
Kurtosis-0.69989816
Mean86.586058
Median Absolute Deviation (MAD)21.735215
Skewness0.2554105
Sum182783.17
Variance685.97748
MonotonicityNot monotonic
2025-12-15T14:10:48.453109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8059
 
2.8%
7043
 
2.0%
5042
 
2.0%
7540
 
1.9%
6037
 
1.8%
6526
 
1.2%
4222
 
1.0%
9020
 
0.9%
7819
 
0.9%
4518
 
0.9%
Other values (1515)1785
84.6%
ValueCountFrequency (%)
391
< 0.1%
39.1018051
< 0.1%
39.3715231
< 0.1%
39.6952951
< 0.1%
39.8501371
< 0.1%
401
< 0.1%
40.2027731
< 0.1%
40.3434631
< 0.1%
41.2201751
< 0.1%
41.2685971
< 0.1%
ValueCountFrequency (%)
1731
< 0.1%
165.0572691
< 0.1%
160.9353511
< 0.1%
160.6394051
< 0.1%
155.8720931
< 0.1%
155.2426721
< 0.1%
154.6184461
< 0.1%
153.9599451
< 0.1%
153.1494911
< 0.1%
152.7205451
< 0.1%

family_history_with_overweight
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
True
1726 
False
385 
ValueCountFrequency (%)
True1726
81.8%
False385
 
18.2%
2025-12-15T14:10:48.637015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

FAVC
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
True
1866 
False
245 
ValueCountFrequency (%)
True1866
88.4%
False245
 
11.6%
2025-12-15T14:10:48.713831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

FCVC
Real number (ℝ)

Distinct810
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4190431
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-12-15T14:10:48.884885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5232145
Q12
median2.385502
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53392658
Coefficient of variation (CV)0.2207181
Kurtosis-0.6375459
Mean2.4190431
Median Absolute Deviation (MAD)0.385502
Skewness-0.43290583
Sum5106.5999
Variance0.28507759
MonotonicityNot monotonic
2025-12-15T14:10:49.132691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3652
30.9%
2600
28.4%
133
 
1.6%
2.9715742
 
0.1%
2.6301372
 
0.1%
2.96732
 
0.1%
2.7583942
 
0.1%
2.8231792
 
0.1%
2.5680632
 
0.1%
2.136832
 
0.1%
Other values (800)812
38.5%
ValueCountFrequency (%)
133
1.6%
1.0035661
 
< 0.1%
1.0055781
 
< 0.1%
1.008761
 
< 0.1%
1.0311491
 
< 0.1%
1.0361591
 
< 0.1%
1.0364141
 
< 0.1%
1.0526991
 
< 0.1%
1.0535341
 
< 0.1%
1.0634491
 
< 0.1%
ValueCountFrequency (%)
3652
30.9%
2.9984411
 
< 0.1%
2.9979511
 
< 0.1%
2.9975241
 
< 0.1%
2.9967171
 
< 0.1%
2.9961861
 
< 0.1%
2.9955991
 
< 0.1%
2.994481
 
< 0.1%
2.9923291
 
< 0.1%
2.9922051
 
< 0.1%

NCP
Real number (ℝ)

Distinct635
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.685628
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-12-15T14:10:49.376891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.658738
median3
Q33
95-th percentile3.750881
Maximum4
Range3
Interquartile range (IQR)0.341262

Descriptive statistics

Standard deviation0.77803865
Coefficient of variation (CV)0.28970454
Kurtosis0.38552662
Mean2.685628
Median Absolute Deviation (MAD)0
Skewness-1.1070973
Sum5669.3608
Variance0.60534414
MonotonicityNot monotonic
2025-12-15T14:10:49.621692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31203
57.0%
1199
 
9.4%
469
 
3.3%
2.776842
 
0.1%
2.6446922
 
0.1%
3.5598412
 
0.1%
1.1046422
 
0.1%
3.9854422
 
0.1%
3.6912262
 
0.1%
1.737622
 
0.1%
Other values (625)626
29.7%
ValueCountFrequency (%)
1199
9.4%
1.0002831
 
< 0.1%
1.0004141
 
< 0.1%
1.000611
 
< 0.1%
1.0013831
 
< 0.1%
1.0015421
 
< 0.1%
1.0016331
 
< 0.1%
1.0053911
 
< 0.1%
1.0094261
 
< 0.1%
1.0103191
 
< 0.1%
ValueCountFrequency (%)
469
3.3%
3.9995911
 
< 0.1%
3.9987661
 
< 0.1%
3.9986181
 
< 0.1%
3.9959571
 
< 0.1%
3.9951471
 
< 0.1%
3.9945881
 
< 0.1%
3.9909251
 
< 0.1%
3.989551
 
< 0.1%
3.9894921
 
< 0.1%

CAEC
Categorical

Imbalance 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size135.9 KiB
Sometimes
1765 
Frequently
242 
Always
 
53
no
 
51

Length

Max length10
Median length9
Mean length8.8702037
Min length2

Characters and Unicode

Total characters18725
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowSometimes
3rd rowSometimes
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes1765
83.6%
Frequently242
 
11.5%
Always53
 
2.5%
no51
 
2.4%

Length

2025-12-15T14:10:49.890594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T14:10:50.031586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sometimes1765
83.6%
frequently242
 
11.5%
always53
 
2.5%
no51
 
2.4%

Most occurring characters

ValueCountFrequency (%)
e4014
21.4%
m3530
18.9%
t2007
10.7%
s1818
9.7%
o1816
9.7%
S1765
9.4%
i1765
9.4%
y295
 
1.6%
l295
 
1.6%
n293
 
1.6%
Other values (7)1127
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)18725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e4014
21.4%
m3530
18.9%
t2007
10.7%
s1818
9.7%
o1816
9.7%
S1765
9.4%
i1765
9.4%
y295
 
1.6%
l295
 
1.6%
n293
 
1.6%
Other values (7)1127
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e4014
21.4%
m3530
18.9%
t2007
10.7%
s1818
9.7%
o1816
9.7%
S1765
9.4%
i1765
9.4%
y295
 
1.6%
l295
 
1.6%
n293
 
1.6%
Other values (7)1127
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e4014
21.4%
m3530
18.9%
t2007
10.7%
s1818
9.7%
o1816
9.7%
S1765
9.4%
i1765
9.4%
y295
 
1.6%
l295
 
1.6%
n293
 
1.6%
Other values (7)1127
 
6.0%

SMOKE
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
False
2067 
True
 
44
ValueCountFrequency (%)
False2067
97.9%
True44
 
2.1%
2025-12-15T14:10:50.129676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

CH2O
Real number (ℝ)

Distinct1268
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0080114
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-12-15T14:10:50.304607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5848125
median2
Q32.47742
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)0.8926075

Descriptive statistics

Standard deviation0.61295345
Coefficient of variation (CV)0.30525397
Kurtosis-0.87939461
Mean2.0080114
Median Absolute Deviation (MAD)0.452986
Skewness-0.10491164
Sum4238.9121
Variance0.37571193
MonotonicityNot monotonic
2025-12-15T14:10:50.554891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2448
 
21.2%
1211
 
10.0%
3162
 
7.7%
1.6363263
 
0.1%
2.8256293
 
0.1%
1.8769152
 
0.1%
2.4500692
 
0.1%
1.4906132
 
0.1%
2.1159672
 
0.1%
2.137552
 
0.1%
Other values (1258)1274
60.4%
ValueCountFrequency (%)
1211
10.0%
1.0004631
 
< 0.1%
1.0005361
 
< 0.1%
1.0005441
 
< 0.1%
1.0006951
 
< 0.1%
1.0013071
 
< 0.1%
1.0019951
 
< 0.1%
1.0022921
 
< 0.1%
1.0030631
 
< 0.1%
1.0035631
 
< 0.1%
ValueCountFrequency (%)
3162
7.7%
2.9994951
 
< 0.1%
2.9945151
 
< 0.1%
2.9934481
 
< 0.1%
2.9916711
 
< 0.1%
2.9893891
 
< 0.1%
2.9887711
 
< 0.1%
2.9877181
 
< 0.1%
2.9874061
 
< 0.1%
2.9843231
 
< 0.1%

SCC
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
False
2015 
True
 
96
ValueCountFrequency (%)
False2015
95.5%
True96
 
4.5%
2025-12-15T14:10:50.723497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

FAF
Real number (ℝ)

Zeros 

Distinct1190
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0102977
Minimum0
Maximum3
Zeros411
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-12-15T14:10:50.926049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.124505
median1
Q31.6666775
95-th percentile2.677133
Maximum3
Range3
Interquartile range (IQR)1.5421725

Descriptive statistics

Standard deviation0.85059243
Coefficient of variation (CV)0.84192257
Kurtosis-0.62058776
Mean1.0102977
Median Absolute Deviation (MAD)0.804157
Skewness0.49848961
Sum2132.7384
Variance0.72350748
MonotonicityNot monotonic
2025-12-15T14:10:51.271035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0411
 
19.5%
1234
 
11.1%
2183
 
8.7%
375
 
3.6%
1.2281362
 
0.1%
1.5410722
 
0.1%
1.6615562
 
0.1%
1.0979052
 
0.1%
0.2453542
 
0.1%
0.2880322
 
0.1%
Other values (1180)1196
56.7%
ValueCountFrequency (%)
0411
19.5%
9.6 × 10-51
 
< 0.1%
0.0002721
 
< 0.1%
0.0004541
 
< 0.1%
0.0010151
 
< 0.1%
0.0010861
 
< 0.1%
0.0012721
 
< 0.1%
0.0012971
 
< 0.1%
0.002031
 
< 0.1%
0.003421
 
< 0.1%
ValueCountFrequency (%)
375
3.6%
2.9999181
 
< 0.1%
2.9989811
 
< 0.1%
2.9718321
 
< 0.1%
2.9397331
 
< 0.1%
2.9365511
 
< 0.1%
2.9315271
 
< 0.1%
2.8929222
 
0.1%
2.8919861
 
< 0.1%
2.891181
 
< 0.1%

TUE
Real number (ℝ)

Zeros 

Distinct1129
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65786592
Minimum0
Maximum2
Zeros557
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-12-15T14:10:51.531039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.62535
Q31
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.60892726
Coefficient of variation (CV)0.92560997
Kurtosis-0.5486604
Mean0.65786592
Median Absolute Deviation (MAD)0.484872
Skewness0.61850241
Sum1388.755
Variance0.37079241
MonotonicityNot monotonic
2025-12-15T14:10:51.877846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0557
26.4%
1292
 
13.8%
2109
 
5.2%
0.6308664
 
0.2%
1.1198773
 
0.1%
0.00263
 
0.1%
0.8285492
 
0.1%
0.1058952
 
0.1%
0.3691342
 
0.1%
1.3392322
 
0.1%
Other values (1119)1135
53.8%
ValueCountFrequency (%)
0557
26.4%
7.3 × 10-51
 
< 0.1%
0.0003551
 
< 0.1%
0.0004361
 
< 0.1%
0.0010961
 
< 0.1%
0.001331
 
< 0.1%
0.0013371
 
< 0.1%
0.0015181
 
< 0.1%
0.001591
 
< 0.1%
0.001641
 
< 0.1%
ValueCountFrequency (%)
2109
5.2%
1.992191
 
< 0.1%
1.9906171
 
< 0.1%
1.9836781
 
< 0.1%
1.9808751
 
< 0.1%
1.9780431
 
< 0.1%
1.9729261
 
< 0.1%
1.971171
 
< 0.1%
1.9695071
 
< 0.1%
1.9672591
 
< 0.1%

CALC
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size131.9 KiB
Sometimes
1401 
no
639 
Frequently
 
70
Always
 
1

Length

Max length10
Median length9
Mean length6.9128375
Min length2

Characters and Unicode

Total characters14593
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowSometimes
3rd rowFrequently
4th rowFrequently
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes1401
66.4%
no639
30.3%
Frequently70
 
3.3%
Always1
 
< 0.1%

Length

2025-12-15T14:10:52.180953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T14:10:52.330524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sometimes1401
66.4%
no639
30.3%
frequently70
 
3.3%
always1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e2942
20.2%
m2802
19.2%
o2040
14.0%
t1471
10.1%
s1402
9.6%
S1401
9.6%
i1401
9.6%
n709
 
4.9%
l71
 
0.5%
y71
 
0.5%
Other values (7)283
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)14593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2942
20.2%
m2802
19.2%
o2040
14.0%
t1471
10.1%
s1402
9.6%
S1401
9.6%
i1401
9.6%
n709
 
4.9%
l71
 
0.5%
y71
 
0.5%
Other values (7)283
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2942
20.2%
m2802
19.2%
o2040
14.0%
t1471
10.1%
s1402
9.6%
S1401
9.6%
i1401
9.6%
n709
 
4.9%
l71
 
0.5%
y71
 
0.5%
Other values (7)283
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2942
20.2%
m2802
19.2%
o2040
14.0%
t1471
10.1%
s1402
9.6%
S1401
9.6%
i1401
9.6%
n709
 
4.9%
l71
 
0.5%
y71
 
0.5%
Other values (7)283
 
1.9%

MTRANS
Categorical

Imbalance 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size155.0 KiB
Public_Transportation
1580 
Automobile
457 
Walking
 
56
Motorbike
 
11
Bike
 
7

Length

Max length21
Median length21
Mean length18.128375
Min length4

Characters and Unicode

Total characters38269
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublic_Transportation
2nd rowPublic_Transportation
3rd rowPublic_Transportation
4th rowWalking
5th rowPublic_Transportation

Common Values

ValueCountFrequency (%)
Public_Transportation1580
74.8%
Automobile457
 
21.6%
Walking56
 
2.7%
Motorbike11
 
0.5%
Bike7
 
0.3%

Length

2025-12-15T14:10:52.484532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T14:10:52.647532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
public_transportation1580
74.8%
automobile457
 
21.6%
walking56
 
2.7%
motorbike11
 
0.5%
bike7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o4096
10.7%
i3691
 
9.6%
t3628
 
9.5%
a3216
 
8.4%
n3216
 
8.4%
r3171
 
8.3%
l2093
 
5.5%
b2048
 
5.4%
u2037
 
5.3%
P1580
 
4.1%
Other values (13)9493
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)38269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o4096
10.7%
i3691
 
9.6%
t3628
 
9.5%
a3216
 
8.4%
n3216
 
8.4%
r3171
 
8.3%
l2093
 
5.5%
b2048
 
5.4%
u2037
 
5.3%
P1580
 
4.1%
Other values (13)9493
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)38269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o4096
10.7%
i3691
 
9.6%
t3628
 
9.5%
a3216
 
8.4%
n3216
 
8.4%
r3171
 
8.3%
l2093
 
5.5%
b2048
 
5.4%
u2037
 
5.3%
P1580
 
4.1%
Other values (13)9493
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)38269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o4096
10.7%
i3691
 
9.6%
t3628
 
9.5%
a3216
 
8.4%
n3216
 
8.4%
r3171
 
8.3%
l2093
 
5.5%
b2048
 
5.4%
u2037
 
5.3%
P1580
 
4.1%
Other values (13)9493
24.8%

NObeyesdad
Categorical

High correlation 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size151.0 KiB
Obesity_Type_I
351 
Obesity_Type_III
324 
Obesity_Type_II
297 
Overweight_Level_I
290 
Overweight_Level_II
290 
Other values (2)
559 

Length

Max length19
Median length16
Mean length16.192326
Min length13

Characters and Unicode

Total characters34182
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal_Weight
2nd rowNormal_Weight
3rd rowNormal_Weight
4th rowOverweight_Level_I
5th rowOverweight_Level_II

Common Values

ValueCountFrequency (%)
Obesity_Type_I351
16.6%
Obesity_Type_III324
15.3%
Obesity_Type_II297
14.1%
Overweight_Level_I290
13.7%
Overweight_Level_II290
13.7%
Normal_Weight287
13.6%
Insufficient_Weight272
12.9%

Length

2025-12-15T14:10:52.824526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T14:10:53.151995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
obesity_type_i351
16.6%
obesity_type_iii324
15.3%
obesity_type_ii297
14.1%
overweight_level_i290
13.7%
overweight_level_ii290
13.7%
normal_weight287
13.6%
insufficient_weight272
12.9%

Most occurring characters

ValueCountFrequency (%)
e5095
14.9%
_3663
 
10.7%
I3059
 
8.9%
i2655
 
7.8%
t2383
 
7.0%
y1944
 
5.7%
O1552
 
4.5%
s1244
 
3.6%
v1160
 
3.4%
g1139
 
3.3%
Other values (17)10288
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)34182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5095
14.9%
_3663
 
10.7%
I3059
 
8.9%
i2655
 
7.8%
t2383
 
7.0%
y1944
 
5.7%
O1552
 
4.5%
s1244
 
3.6%
v1160
 
3.4%
g1139
 
3.3%
Other values (17)10288
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)34182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5095
14.9%
_3663
 
10.7%
I3059
 
8.9%
i2655
 
7.8%
t2383
 
7.0%
y1944
 
5.7%
O1552
 
4.5%
s1244
 
3.6%
v1160
 
3.4%
g1139
 
3.3%
Other values (17)10288
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)34182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5095
14.9%
_3663
 
10.7%
I3059
 
8.9%
i2655
 
7.8%
t2383
 
7.0%
y1944
 
5.7%
O1552
 
4.5%
s1244
 
3.6%
v1160
 
3.4%
g1139
 
3.3%
Other values (17)10288
30.1%

Interactions

2025-12-15T14:10:44.269682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:33.007834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:34.613442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:36.212110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:37.894825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:39.511174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:40.885882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:42.459827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:44.463582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:33.265843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:34.838406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:36.395059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:38.115490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:39.661131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:41.074925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:42.642092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:44.684768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:33.424690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:35.033570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:36.568062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:38.328490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:39.828940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:41.278295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:42.820996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:44.852773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:33.597801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:35.233385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:36.756785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:38.543500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:40.003926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:41.458080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:43.033053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:45.094840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:33.835803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:35.389393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:36.935102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:38.727490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:40.192079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:41.677702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:43.456609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:45.336844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:34.015308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:35.604401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:37.104104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:38.916047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:40.355081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:41.865206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:43.690094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:45.547123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:34.212626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:35.774392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:37.497964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:39.099347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:40.544057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:42.052422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:43.897465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:45.719131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:34.425415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:35.950496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:37.687043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:39.296360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:40.687845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:42.265519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T14:10:44.094335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-15T14:10:53.439382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeCAECCALCCH2OFAFFAVCFCVCGenderHeightMTRANSNCPNObeyesdadSCCSMOKETUEWeightfamily_history_with_overweight
Age1.0000.1570.1630.013-0.2080.1380.0620.196-0.0030.350-0.1060.2960.1410.178-0.2980.3570.239
CAEC0.1571.0000.0980.1830.1170.1930.1300.1310.1510.0950.1670.3520.1600.0460.1330.3180.349
CALC0.1630.0981.0000.1070.1110.1370.0990.0330.0980.0950.1210.2250.0550.1040.1380.2190.012
CH2O0.0130.1830.1071.0000.1560.1950.0660.2380.2250.0900.0700.2350.1310.0750.0230.2260.233
FAF-0.2080.1170.1110.1561.0000.1560.0280.2650.3260.1150.1450.2120.1000.0680.051-0.0440.159
FAVC0.1380.1930.1370.1950.1561.0000.0880.0600.2120.2010.0420.3280.1860.0400.1710.2930.205
FCVC0.0620.1300.0990.0660.0280.0881.0000.347-0.0560.1050.0860.2930.0940.000-0.0880.2080.121
Gender0.1960.1310.0330.2380.2650.0600.3471.0000.6160.1620.1620.5560.0980.0350.1310.3960.099
Height-0.0030.1510.0980.2250.3260.212-0.0560.6161.0000.0860.2040.2040.1740.1770.0820.4630.293
MTRANS0.3500.0950.0950.0900.1150.2010.1050.1620.0861.0000.0400.1790.0700.0000.1260.1400.118
NCP-0.1060.1670.1210.0700.1450.0420.0860.1620.2040.0401.0000.2450.0450.0280.0870.0030.190
NObeyesdad0.2960.3520.2250.2350.2120.3280.2930.5560.2040.1790.2451.0000.2350.1110.2170.5750.540
SCC0.1410.1600.0550.1310.1000.1860.0940.0980.1740.0700.0450.2351.0000.0330.1290.2350.181
SMOKE0.1780.0460.1040.0750.0680.0400.0000.0350.1770.0000.0280.1110.0331.0000.0580.1290.000
TUE-0.2980.1330.1380.0230.0510.171-0.0880.1310.0820.1260.0870.2170.1290.0581.000-0.0500.188
Weight0.3570.3180.2190.226-0.0440.2930.2080.3960.4630.1400.0030.5750.2350.129-0.0501.0000.557
family_history_with_overweight0.2390.3490.0120.2330.1590.2050.1210.0990.2930.1180.1900.5400.1810.0000.1880.5571.000

Missing values

2025-12-15T14:10:46.018046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-15T14:10:46.303058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad
0Female21.01.6264.0yesno2.03.0Sometimesno2.0no0.01.0noPublic_TransportationNormal_Weight
1Female21.01.5256.0yesno3.03.0Sometimesyes3.0yes3.00.0SometimesPublic_TransportationNormal_Weight
2Male23.01.8077.0yesno2.03.0Sometimesno2.0no2.01.0FrequentlyPublic_TransportationNormal_Weight
3Male27.01.8087.0nono3.03.0Sometimesno2.0no2.00.0FrequentlyWalkingOverweight_Level_I
4Male22.01.7889.8nono2.01.0Sometimesno2.0no0.00.0SometimesPublic_TransportationOverweight_Level_II
5Male29.01.6253.0noyes2.03.0Sometimesno2.0no0.00.0SometimesAutomobileNormal_Weight
6Female23.01.5055.0yesyes3.03.0Sometimesno2.0no1.00.0SometimesMotorbikeNormal_Weight
7Male22.01.6453.0nono2.03.0Sometimesno2.0no3.00.0SometimesPublic_TransportationNormal_Weight
8Male24.01.7864.0yesyes3.03.0Sometimesno2.0no1.01.0FrequentlyPublic_TransportationNormal_Weight
9Male22.01.7268.0yesyes2.03.0Sometimesno2.0no1.01.0noPublic_TransportationNormal_Weight
GenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad
2101Female25.7220041.628470107.218949yesyes3.03.0Sometimesno2.487070no0.0673290.455823SometimesPublic_TransportationObesity_Type_III
2102Female25.7656281.627839108.107360yesyes3.03.0Sometimesno2.320068no0.0452460.413106SometimesPublic_TransportationObesity_Type_III
2103Female21.0168491.724268133.033523yesyes3.03.0Sometimesno1.650612no1.5376390.912457SometimesPublic_TransportationObesity_Type_III
2104Female21.6823671.732383133.043941yesyes3.03.0Sometimesno1.610768no1.5103980.931455SometimesPublic_TransportationObesity_Type_III
2105Female21.2859651.726920131.335786yesyes3.03.0Sometimesno1.796267no1.7283320.897924SometimesPublic_TransportationObesity_Type_III
2106Female20.9768421.710730131.408528yesyes3.03.0Sometimesno1.728139no1.6762690.906247SometimesPublic_TransportationObesity_Type_III
2107Female21.9829421.748584133.742943yesyes3.03.0Sometimesno2.005130no1.3413900.599270SometimesPublic_TransportationObesity_Type_III
2108Female22.5240361.752206133.689352yesyes3.03.0Sometimesno2.054193no1.4142090.646288SometimesPublic_TransportationObesity_Type_III
2109Female24.3619361.739450133.346641yesyes3.03.0Sometimesno2.852339no1.1391070.586035SometimesPublic_TransportationObesity_Type_III
2110Female23.6647091.738836133.472641yesyes3.03.0Sometimesno2.863513no1.0264520.714137SometimesPublic_TransportationObesity_Type_III

Duplicate rows

Most frequently occurring

GenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad# duplicates
7Male21.01.6270.0noyes2.01.0nono3.0no1.00.0SometimesPublic_TransportationOverweight_Level_I15
3Female21.01.5242.0noyes3.01.0Frequentlyno1.0no0.00.0SometimesPublic_TransportationInsufficient_Weight4
0Female16.01.6658.0nono2.01.0Sometimesno1.0no0.01.0noWalkingNormal_Weight2
2Female21.01.5242.0nono3.01.0Frequentlyno1.0no0.00.0SometimesPublic_TransportationInsufficient_Weight2
1Female18.01.6255.0yesyes2.03.0Frequentlyno1.0no1.01.0noPublic_TransportationNormal_Weight2
4Female22.01.6965.0yesyes2.03.0Sometimesno2.0no1.01.0SometimesPublic_TransportationNormal_Weight2
5Female25.01.5755.0noyes2.01.0Sometimesno2.0no2.00.0SometimesPublic_TransportationNormal_Weight2
6Male18.01.7253.0yesyes2.03.0Sometimesno2.0no0.02.0SometimesPublic_TransportationInsufficient_Weight2
8Male22.01.7475.0yesyes3.03.0Frequentlyno1.0no1.00.0noAutomobileNormal_Weight2